Enterprises building AI apps often face an uncomfortable choice: hand sensitive data to a third-party managed service—or shoulder the operational burden of running infrastructure themselves.

Zilliz is betting that companies are done choosing.

The company behind the open-source vector database Milvus has announced general availability of Zilliz Cloud BYOC (Bring Your Own Cloud) on Microsoft Azure. With the move, Zilliz Cloud BYOC is now available across Amazon Web Services, Google Cloud Platform, and Azure—making Zilliz the first managed vector database provider to support BYOC on all three hyperscale clouds.

In a market crowded with vector database startups and AI infrastructure vendors, that’s more than a checkbox update. It’s a strategic signal about where enterprise AI infrastructure is heading.

Why BYOC Is Becoming Table Stakes

Vector databases are foundational to modern AI workloads—from semantic search and recommendation engines to retrieval-augmented generation (RAG) pipelines. As enterprises scale generative AI pilots into production, they need search infrastructure that can index and query embeddings efficiently.

But they also need to meet strict compliance, governance, and data residency requirements—especially in regulated sectors like financial services, healthcare, and public sector.

Traditionally, managed services require customers to move data into the vendor’s cloud account. That simplifies operations but complicates compliance. Self-hosted deployments solve the data control problem but demand DevOps muscle many teams would rather apply elsewhere.

Zilliz Cloud BYOC attempts to split the difference. Instead of hosting customer data in its own environment, Zilliz deploys a fully managed vector database directly inside the customer’s cloud account. The company manages the service; the customer retains full control over the infrastructure and data perimeter.

“The AI infrastructure landscape is at an inflection point,” said Charles Xie, founder and CEO of Zilliz, in a statement accompanying the launch. “Enterprises need platforms that respect their security, compliance, and multi-cloud realities.”

Translation: speed without surrender.

The Azure Factor

The Azure launch is arguably the most strategic piece of the rollout.

Zilliz previously expanded BYOC support from AWS to GCP. Adding Azure closes the loop for enterprises standardized on Microsoft’s ecosystem—particularly those building AI apps alongside Azure-native services like Azure OpenAI and other components of the Azure AI stack.

Keeping vector search infrastructure inside the same cloud environment reduces cross-cloud data movement, simplifies billing, and avoids potential latency overhead. For enterprises running large-scale AI workloads, that’s not just a technical detail—it’s a cost and governance consideration.

Azure customers can also align BYOC deployments with existing enterprise agreements, reserved capacity commitments, and internal governance frameworks. That means procurement, compliance, and infrastructure teams don’t have to carve out exceptions to adopt a new AI component.

In short: fewer internal battles, faster production timelines.

Automation and Enterprise-Grade Ops

Zilliz is also emphasizing operational maturity. With an official Zilliz Cloud Terraform Provider, enterprises can deploy and manage BYOC environments through infrastructure-as-code workflows—integrating vector search into existing CI/CD pipelines.

That’s critical for organizations operating at scale. AI infrastructure that can’t plug into established DevOps practices tends to stall in proof-of-concept purgatory.

The broader message here is that vector databases are no longer experimental add-ons. They’re becoming core infrastructure—expected to meet the same reliability, observability, and automation standards as traditional databases.

Competitive Context: Pinecone and the Rest

Zilliz enters a competitive field that includes managed-first vector database players and open-source alternatives.

The company is explicitly positioning BYOC as a differentiator against fully managed SaaS models from vendors like Pinecone, as well as open-source-centric players such as Qdrant and Weaviate. It also highlights migration paths from legacy systems like Elasticsearch, PostgreSQL, and OpenSearch.

That migration story matters. Many enterprises initially experimented with vector search inside general-purpose databases or search engines. As AI workloads grow, those stopgap solutions often hit scaling or performance limits, prompting a move to purpose-built vector infrastructure.

By offering BYOC across all major clouds, Zilliz is effectively telling CIOs: you don’t need to re-platform your cloud strategy to standardize on our database.

What This Means for Enterprise AI

The implications extend beyond vendor bragging rights.

As generative AI moves from prototype to production, infrastructure decisions are becoming more strategic. Data sovereignty regulations are tightening globally. Boards are asking pointed questions about AI governance. And multi-cloud strategies remain common among large enterprises.

In that environment, the ability to:

• Keep data inside a specific jurisdiction

• Align with existing cloud billing and contracts

• Avoid cross-cloud egress fees

• Standardize vector infrastructure across teams

…isn’t a nice-to-have. It’s a prerequisite.

BYOC models reflect a broader shift in enterprise software: customers want managed convenience without losing architectural control.

Now Live Across the Big Three

With Azure support now generally available, Zilliz Cloud BYOC spans AWS, GCP, and Azure. Every deployment includes the full Zilliz Cloud feature set built on Milvus, along with tooling for migration from competing vector databases and search platforms.

For enterprises under pressure to accelerate AI adoption—without triggering compliance alarms or ballooning infrastructure complexity—that combination may prove compelling.

The next competitive frontier won’t just be performance benchmarks. It will be how seamlessly vector infrastructure fits into the messy, multi-cloud reality of enterprise IT.

Zilliz just made a clear bet on where that future is headed.

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Manufacturers have spent the past few years grappling with supply chain shocks and workforce volatility. Now, a new threat is looming larger: the slow but steady loss of institutional knowledge.

That’s the focus of an upcoming March 3, 2026 webinar from Intellect, hosted by CEO Heather Preu and featuring analysts Allison Kuhn and James Wells from LNS Research. The session, titled Navigating the Chaos of Manufacturing in 2026, zeroes in on a challenge that’s quickly becoming existential for industrial and life sciences firms: how to preserve operational expertise before it walks out the door.

The webinar arrives on the heels of what many in the industry describe as one of the toughest recall years in recent memory. In 2025, manufacturers faced mounting product recalls, compliance scrutiny, and operational disruptions—often tied not to a lack of data, but to disconnected systems and fractured processes.

The Real Risk: Institutional Amnesia

As experienced operators, engineers, and quality leaders retire or transition roles, decades of tacit knowledge—how to troubleshoot a finicky line, how to interpret subtle quality deviations, how to navigate compliance gray areas—can vanish with them.

According to Intellect, this loss isn’t just a talent issue; it’s a structural risk to quality, compliance, and production continuity.

The March 3 discussion will examine how AI-driven platforms can capture frontline expertise and convert it into reusable, scalable operational intelligence. The premise: if knowledge can be codified, connected to execution systems, and embedded into workflows, it doesn’t disappear when a veteran worker does.

That’s a sharp departure from traditional manufacturing IT architectures, where Quality Management Systems (QMS), frontline worker tools, and operational data often sit in separate silos.

From Fragmented Systems to Unified Platforms

A central theme of the webinar is the convergence of QMS and Connected Frontline Worker technologies. Rather than treating compliance and execution as parallel tracks, modern manufacturers are increasingly seeking integrated systems that unify them.

Preu is expected to discuss how customer demand for this integration has shaped Intellect’s acquisition strategy—specifically its push to combine quality management and frontline execution into a single operational framework. The goal: link compliance data, production workflows, and institutional knowledge in one AI-enabled environment.

It’s a move that mirrors broader industry trends. As AI adoption accelerates in manufacturing, companies are moving beyond isolated predictive maintenance pilots and toward enterprise-wide operational intelligence. The focus is shifting from dashboards to decision-making—embedding insights directly into frontline workflows.

When quality events, deviations, and corrective actions are digitally connected to shop-floor execution, manufacturers gain more than traceability. They gain context.

Why This Matters Now

The timing of this conversation is no accident.

Life sciences and industrial manufacturers are under intensifying regulatory pressure, while operating with thinner margins and more complex global supply networks. Recalls don’t just hurt financially—they damage brand equity and invite long-term scrutiny.

At the same time, digital transformation initiatives are entering a new phase. Early adopters have already digitized documentation and basic workflows. The next frontier is intelligence: systems that don’t just record events, but actively guide decisions.

That’s where AI becomes less about hype and more about resilience.

If knowledge from seasoned operators can be embedded into digital workflows—automating best practices, flagging risks, standardizing responses—new hires can ramp faster, quality deviations can be caught earlier, and compliance gaps can be closed before they trigger audits or recalls.

In other words, AI shifts from experimental to operational.

Analyst Perspective: 2026 as an Inflection Point

Kuhn and Wells are expected to provide independent analysis on how workforce transformation, AI adoption, and digital platform consolidation are reshaping performance expectations across manufacturing and life sciences.

Industry analysts have increasingly framed 2026 as a pivotal year: demographic shifts are accelerating, regulatory environments are tightening, and boards are demanding measurable ROI from digital investments.

For vendors, that means platform narratives must translate into tangible outcomes—fewer recalls, faster onboarding, improved yield, and lower compliance risk.

For manufacturers, it means rethinking architecture. Point solutions may still solve local problems, but disconnected systems create blind spots. Unified platforms promise continuity—of data, of processes, and of expertise.

A Subtle Competitive Signal

While positioned as an educational event, the webinar also underscores Intellect’s broader market positioning: AI-native, unified, and purpose-built for regulated manufacturing.

The emphasis on integrating QMS and frontline execution places the company in direct conversation with larger enterprise software providers and niche connected worker vendors alike. The differentiator, according to Intellect, is natively linking quality data with real-time execution in one system rather than stitching tools together post-deployment.

Whether that approach becomes the dominant model remains to be seen. But as recalls mount and workforce churn persists, manufacturers are clearly reassessing legacy architectures.

What Attendees Can Expect

The on-demand webinar will explore:

• Strategies to capture and preserve institutional expertise

• How AI can convert operational data into actionable intelligence

• Approaches to reducing recall risk through unified platforms

• The evolving role of QMS in a connected workforce environment

Manufacturing, quality, and operations leaders navigating digital transformation initiatives in 2026 may find the discussion particularly timely.

Because in today’s environment, the real chaos isn’t just external volatility—it’s what happens when critical knowledge is fragmented, disconnected, or gone entirely.

Get in touch with our MarTech Experts.

In the enterprise AI arms race, most organizations aren’t blocked by algorithms—they’re blocked by their own data.

That’s the premise behind a new strategic partnership between BonData, which bills itself as the creator of the first “Smart Harmonization Layer” for complex enterprise ecosystems, and Elad Systems (TASE: ELAD), a long-established digital transformation consultancy.

The companies say the collaboration will help enterprises across North America and EMEA bypass traditional integration bottlenecks by automating the correlation of fragmented data across legacy systems and SaaS applications—ultimately delivering what they describe as a unified “Golden Record” in days rather than months.

It’s a bold promise in a market where data unification projects are notorious for dragging on long after executive enthusiasm fades.

From “Data Debt” to Decision Latency

The partnership is anchored in a concept BonData calls “Data Debt”—the accumulation of fragmented, inconsistent, and poorly reconciled data across ERP systems, CRMs, data warehouses, and cloud apps.

While the term echoes technical debt in software engineering, its business consequence is what BonData labels “Decision Latency.” In plain terms: by the time leadership reconciles conflicting reports from finance, operations, and sales, the market opportunity has already shifted.

This isn’t theoretical. In volatile markets—whether driven by supply chain disruption, regulatory shifts, or AI-fueled competitive pressure—the speed of decision-making increasingly determines winners and losers.

Yet many enterprises still rely on brittle ETL pipelines, manual reconciliation, and spreadsheet-based workarounds to answer basic cross-functional questions.

BonData’s pitch is that harmonization—not just integration—is the missing layer.

IntelliBond and the “Surgical Wedge” Approach

At the center of the partnership is BonData’s IntelliBond engine, which automates the correlation of disparate data entities across systems. Rather than forcing organizations into massive rip-and-replace modernization efforts, the platform uses what the company calls a “Surgical Wedge” approach.

The idea: insert a harmonization layer that sits across existing infrastructure, mapping and reconciling entities without overhauling core systems.

For implementation partner Elad Systems, this changes the delivery model. Instead of spending months in what Brandes describes as the “Data Janitor” phase—cleaning, deduplicating, and reconciling records—teams can focus earlier on higher-value business use cases.

The outcome, according to the companies, is a unified, real-time “Golden Record” that aligns board-level reporting, operational dashboards, and AI models on the same verified source of truth.

In theory, that reduces internal friction as much as it improves analytics accuracy.

Why This Matters in the AI Era

The timing of the partnership is no accident.

As enterprises push to operationalize AI—particularly large language models and predictive analytics—data quality has emerged as the unglamorous bottleneck. “Garbage in, garbage out” remains stubbornly true, even in the age of generative AI.

BonData and Elad are positioning their joint offering as a foundation for what they call “High-Fidelity AI Readiness.” By automating entity correlation and building a contextual semantic map of enterprise data, organizations can move from experimental AI pilots to operational systems that act on harmonized context.

That’s a key distinction. Many AI initiatives stall after proof-of-concept because the underlying data is inconsistent or incomplete. A forecasting model trained on fragmented customer data won’t suddenly become strategic just because it’s powered by an LLM.

In this framing, harmonization becomes not just a data governance initiative, but a prerequisite for enterprise-grade AI.

Business Outcomes Over Buzzwords

The partnership outlines three primary business objectives:

Reduced Decision Latency: Compressing the time between raw data and executive action, particularly in fast-moving markets.

Elimination of “Data Doubt”: Ensuring that finance, operations, and the board are working from the same reconciled metrics rather than competing dashboards.

Accelerated ROI: By shortening integration cycles, clients can see value sooner—an increasingly important factor as IT budgets face scrutiny.

While the language leans aspirational, the underlying value proposition is straightforward: fewer months spent wrangling data, more time extracting insight.

That positioning puts BonData in a competitive landscape that includes data integration platforms, master data management (MDM) vendors, and newer semantic-layer startups. What differentiates it, the company argues, is automation at the entity-correlation level rather than rule-heavy data cleansing or manual mapping.

For Elad Systems, the partnership enhances its ability to deliver transformation programs with faster payback periods—an attractive pitch in an era when digital transformation fatigue is real.

The Broader Market Context

Enterprise ecosystems are more complex than ever. Organizations often operate dozens—or hundreds—of interconnected applications spanning on-premises systems and modern SaaS tools. Each generates its own data schema, definitions, and logic.

Historically, solving this required large-scale data warehouse projects or monolithic ERP consolidation. Today, many CIOs are reluctant to embark on multi-year overhauls with uncertain ROI.

A harmonization layer that overlays existing infrastructure may be more palatable—especially if it can deliver measurable improvements in days.

Still, execution will be critical. Automated entity matching across heterogeneous systems is notoriously difficult, particularly in regulated industries where precision matters.

If BonData’s IntelliBond engine can reliably reduce reconciliation time without introducing new inconsistencies, it could find traction among enterprises eager to accelerate AI adoption without another infrastructure rebuild.

Speed as a Competitive Advantage

Caroline Meidan, CEO of BonData, frames the partnership in stark terms: the most expensive asset in a modern enterprise is a slow, inaccurate decision.

In volatile markets, speed and accuracy are no longer trade-offs. Boards expect both.

By combining BonData’s harmonization technology with Elad Systems’ implementation expertise, the companies are betting that the fastest path to AI maturity isn’t another dashboard—it’s cleaner, unified context beneath it.

If they’re right, the next wave of enterprise AI won’t be defined by bigger models. It will be defined by better-aligned data.

Get in touch with our MarTech Experts.

Melbourne-based AI agency Enterprise Monkey announced it will transition all internal AI operations, agents, and new product development to Claude, the flagship model developed by Anthropic.

The company said the move follows growing concerns around platform direction and governance in the AI sector, alongside what it described as a technical preference for Claude in agentic AI deployments.

Ethical Concerns and Platform Direction

Enterprise Monkey’s decision comes amid broader industry debate surrounding AI governance, commercialization models, and regulatory pressures.

CEO Aamir Qutub framed the shift as both a values-based and strategic move, stating that companies must take clear positions when governments or corporations push AI toward controversial use cases. He referenced recent geopolitical tensions involving AI providers and regulatory scrutiny impacting model deployment policies.

The announcement also coincides with the rise of the #QuitGPT movement on X, which has reportedly generated significant online engagement and user migration discussions.

Technical Rationale: “Purpose-Built for Agentic AI”

Qutub emphasized that the shift was not solely ideological.

According to the company, Claude offers stronger performance for:

• Autonomous AI agents

• Model Context Protocol (MCP) integrations

• Native tool use

• Structured reasoning workflows

Enterprise Monkey develops AI agents designed to autonomously manage business functions, including CRM, email workflows, media outreach, and content production.

Its proprietary agent, Zee, already operates entirely on Claude infrastructure.

“When your agents are making real business decisions, accuracy is everything,” Qutub said, citing concerns about hallucination rates and reasoning consistency in competing models.

Client Recommendations Remain Platform-Agnostic

Despite the internal transition, Enterprise Monkey clarified that it will continue recommending solutions based on client needs.

The agency stated it will:

• Continue building on OpenAI products where appropriate

• Advocate for Microsoft Copilot 365 in enterprise productivity environments

• Maintain platform independence in consulting engagements

“Our job is to give clients the best advice, full stop,” Qutub said. “We’re not in the business of pushing platforms — we’re in the business of solving problems.”

Book Revision Reflects Strategic Shift

Qutub, author of The CEO Who Mocked AI (Until It Made Him Millions), confirmed he is revising the book’s upcoming edition to reflect the agency’s platform shift. References to ChatGPT will be replaced with Claude, and the narrative will expand to include themes around ethical AI and sovereign alternatives.

Broader Industry Context

The move highlights a growing divide in the AI ecosystem:

• Some companies prioritize ecosystem scale, integrations, and commercial distribution.

• Others emphasize safety positioning, governance stance, and technical specialization in agentic AI.

As AI agencies increasingly build autonomous systems that execute business-critical tasks, model reliability, reasoning transparency, and governance philosophy are becoming strategic differentiators—not just technical specifications.

For Enterprise Monkey, the transition signals where it plans to concentrate its R&D investment and long-term intellectual property development.

Whether similar agencies follow suit may depend less on online movements and more on measurable performance in real-world, revenue-impacting AI systems.

Get in touch with our MarTech Experts.

Intuit Inc. (Nasdaq: INTU) delivered a strong second quarter for fiscal 2026, reporting revenue growth of 17% year-over-year to $4.7 billion for the period ending January 31, as the company continues expanding its AI-driven financial platform strategy.

CEO Sasan Goodarzi highlighted the company’s focus on what he described as a new category at the intersection of AI and human intelligence, emphasizing “autonomous, done-for-you experiences” across tax, small business, and mid-market enterprise solutions.

Q2 FY26 Financial Highlights

Revenue: $4.651 billion, up 17%
GAAP Operating Income: $855 million, up 44%
Non-GAAP Operating Income: $1.549 billion, up 23%
GAAP Diluted EPS: $2.48, up 49%
Non-GAAP Diluted EPS: $4.15, up 25%

The margin expansion reflects disciplined cost management alongside top-line growth.

CFO Sandeep Aujla said momentum across the company’s “big bets” gives management high confidence in delivering double-digit revenue growth and margin expansion for the full fiscal year.

Segment Performance

Global Business Solutions (GBS)

GBS revenue rose 18% to $3.2 billion.

• Online Ecosystem revenue: $2.5 billion, up 21%

• Excluding Mailchimp, GBS grew 21%

• Online Ecosystem revenue excluding Mailchimp grew 25%

Key drivers:

• QuickBooks Online Accounting revenue increased 24%, fueled by pricing, customer growth, and product mix shift.

• Online Services revenue grew 18%, driven by payroll and money offerings.

• International online revenue rose 9% on a constant currency basis.

Intuit noted that Mailchimp is expected to return to double-digit growth beyond fiscal 2026.

Consumer Segment

Consumer revenue increased 15% to $1.5 billion.

• Credit Karma revenue: $616 million, up 23%

• TurboTax revenue: $581 million, up 12%

• ProTax revenue: $290 million, up 7%

Credit Karma benefited from strong demand in personal loans, credit cards, and auto insurance, while TurboTax growth reflects ongoing digital tax adoption.

Capital Allocation & Balance Sheet

As of January 31, 2026:

• Cash and investments: ~$3.0 billion

• Debt: $6.2 billion

Key actions:

• Entered and subsequently terminated a $5.8 billion revolving credit facility tied to TurboTax early refund offerings.

• Replaced a prior credit agreement with a new $2.2 billion unsecured revolving credit facility maturing in 2031.

• Repurchased $961 million in shares during the quarter.

• $3.5 billion remains under the current repurchase authorization.

• Approved a quarterly dividend of $1.20 per share, payable April 17, 2026 — a 15% increase year-over-year.

Full-Year FY26 Guidance (Reiterated)

Intuit reaffirmed its fiscal 2026 outlook:

• Revenue: $20.997B–$21.186B (12–13% growth)

• GAAP Operating Income: $5.782B–$5.859B (17–19% growth)

• Non-GAAP Operating Income: $8.611B–$8.688B (14–15% growth)

• GAAP EPS: $15.49–$15.69 (13–15% growth)

• Non-GAAP EPS: $22.98–$23.18 (14–15% growth)

Segment Outlook

• Global Business Solutions: 14–15% growth

• Consumer: 8–9% growth

  • TurboTax: ~8%

  • Credit Karma: 10–13%

  • ProTax: 2–3%

Q3 FY26 Guidance

For the quarter ending April 30:

• Revenue growth of approximately 10%

• GAAP EPS: $10.56–$10.62

• Non-GAAP EPS: $12.45–$12.51

Strategic Context

Intuit’s results underscore continued execution across its AI-enabled platform strategy, integrating financial software, consumer finance, marketing automation, and enterprise solutions.

The company is increasingly positioning itself not just as a financial software provider, but as an AI-native financial technology ecosystem spanning consumers, small businesses, and mid-market enterprises.

With double-digit revenue growth, expanding margins, and reiterated guidance, Intuit enters the second half of fiscal 2026 with strong operational momentum.

Get in touch with our MarTech Experts.

Orange Business and Tech Mahindra are entering exclusive negotiations to form a non-equity global strategic partnership aimed at accelerating enterprise digital transformation—and reshaping how Orange Business delivers services outside France.

If finalized, the agreement would combine Orange Business’ secure connectivity platforms and global enterprise footprint with Tech Mahindra’s delivery scale and operational agility. The move signals a pragmatic pivot: deepen international reach while streamlining operations through selective outsourcing.

At stake is more than cost optimization. Orange Business is positioning the partnership as a growth lever in its ambition to become what it calls the “undisputed worldwide leader in secure connectivity for enterprises.”

A Go-to-Market Reset for Global Growth

The proposed collaboration centers on a joint go-to-market strategy focused on regional expansion, product innovation, and greater utilization of Orange Business’ existing platforms to deliver AI-powered, secure, and scalable solutions.

Enterprise demand for integrated connectivity, cybersecurity, cloud, and AI services is accelerating—particularly among multinationals seeking standardized services across regions. Yet scaling globally while maintaining margins has proven difficult for telecom-affiliated enterprise units.

By partnering with Tech Mahindra, Orange Business aims to expand faster in international markets while maintaining direct control over certain critical segments, including its French operations and regulated environments.

Notably, Orange Business would continue to ensure compliance with French and European regulations—a crucial point in a region where data sovereignty and labor frameworks are tightly regulated.

Outsourcing as Strategic Lever

A significant component of the proposal involves outsourcing parts of Orange Business’ global customer support, quote-to-bill operations, and post-sales teams outside France to Tech Mahindra.

In telecom and enterprise IT services, quote-to-bill workflows are complex and often slow, spanning proposal generation, pricing, contracting, provisioning, and invoicing. Improving speed and automation in this chain can directly affect customer satisfaction and revenue realization.

For Orange Business, shifting certain operational functions to Tech Mahindra could increase efficiency and scalability, particularly in price-sensitive global markets. For Tech Mahindra, the arrangement strengthens its footprint in managed services and telecom enterprise operations.

The companies emphasized that the partnership is non-equity—suggesting operational integration without ownership entanglements.

Complementary Strengths, Competitive Pressures

The partnership aligns two distinct but complementary capabilities.

Orange Business brings global network infrastructure, secure connectivity platforms, and an established enterprise brand. Tech Mahindra contributes large-scale delivery capabilities, automation expertise, and deep experience in telecom IT transformation.

The enterprise connectivity market is intensely competitive. Global systems integrators and hyperscalers increasingly encroach on telecom operators’ enterprise turf, offering cloud-native networking and AI-driven platforms.

To compete, telecom enterprise arms must move faster, innovate in AI-driven services, and operate with systems-integrator-like efficiency. The proposed partnership reflects that reality.

Aliette Mousnier-Lompré, CEO of Orange Business, described the collaboration as a growth catalyst designed to expand both market reach and operational excellence. Tech Mahindra CEO Mohit Joshi framed it as an opportunity to “shape the future of enterprise connectivity and digital experiences.”

Behind the rhetoric lies a clear strategic calculus: combine infrastructure ownership with scalable service delivery to stay competitive against global IT services giants.

Automation and AI at the Core

Both companies highlighted automation and AI as central pillars.

A comprehensive operational review is planned to identify areas where Tech Mahindra’s know-how can streamline processes and accelerate delivery. The emphasis is on speed, scalability, and customer experience—hallmarks of modern digital transformation mandates.

For enterprises, the promise is integrated connectivity and AI-enabled services delivered with greater agility. For Orange Business, it’s about maintaining relevance as enterprise customers increasingly expect software-defined, API-driven, and AI-orchestrated services.

Regulatory and Workforce Considerations

The project remains subject to consultation with relevant employee representative bodies—a standard but significant step in France and across Europe.

Labor considerations often influence the structure and timing of such partnerships. By retaining critical French operations while outsourcing select global functions, Orange Business appears to be balancing growth ambitions with domestic regulatory and workforce realities.

Strategic Context: Telecom Enterprise Units Under Pressure

Telecom operators’ enterprise divisions face a dual challenge: declining traditional connectivity margins and rising expectations for integrated digital solutions.

Some operators have spun off enterprise units. Others have leaned heavily into partnerships with IT services firms to extend capabilities without massive in-house expansion.

This proposed alliance places Orange Business firmly in the latter camp.

If completed, the partnership could serve as a template for telecom enterprise divisions seeking to scale globally without diluting regulatory control at home.

What Happens Next

The companies are currently in exclusive negotiations, meaning terms are being finalized and due diligence continues. The outcome depends on internal approvals and employee consultation processes.

Should the deal close, the partnership would mark a significant operational evolution for Orange Business—less a traditional telecom enterprise arm, more a hybrid infrastructure-and-delivery ecosystem.

In a market where secure connectivity increasingly converges with AI-driven services, scale and speed matter as much as network reach.

Orange Business is betting that together with Tech Mahindra, it can deliver both.

Get in touch with our MarTech Experts.

At this year’s Mobile World Congress Barcelona, three telecom heavyweights laid out a plan to fix one of the industry’s most stubborn pain points: international roaming failures that leave travelers staring at a lifeless signal bar.

NTT DOCOMO, StarHub, and ServiceNow announced a joint initiative to introduce autonomous roaming resolution powered by ServiceNow CRM and the ServiceNow AI Platform. The trio says they are building the industry’s first inter-carrier operational model designed to automatically identify and resolve roaming issues across network boundaries—in real time.

If successful, the project could transform how carriers handle one of the most complex cross-operator workflows in telecom.

The Roaming Problem No One “Owns”

When a customer loses mobile service overseas, the failure rarely sits within a single network. It may involve the home operator, the visited operator, signaling gateways, authentication systems, and clearinghouses. Today, coordination between carriers often relies on fragmented intake channels—web forms, emails, and proprietary portals.

There is no universal, standardized workflow.

For travelers, that can mean hours—or days—without service when they need maps, ride-hailing, or two-factor authentication. For operators, the cost shows up in churn, lost roaming revenue, and brand damage.

In an era where 5G promises ultra-reliability, roaming remains surprisingly manual.

Extending Zero-Touch Beyond a Single Network

DOCOMO has been working with ServiceNow since 2021 to automate internal operations through Zero-Touch Operation (ZTO), eliminating manual intervention in many remote maintenance tasks. That effort reduced fault recovery times and even removed the need for certain overnight support shifts.

Now, the companies are extending that automation model across carrier boundaries.

Instead of handling inter-carrier roaming faults as ad hoc escalations, the new initiative turns them into AI-driven workflows orchestrated on the ServiceNow AI Platform. The system automatically shares structured fault information between participating carriers, tracks resolution progress, and provides real-time visibility into root cause analysis.

In effect, it treats multi-operator troubleshooting as a unified operational domain rather than a patchwork of bilateral agreements.

How the AI ‘Control Tower’ Works

At the core of the initiative is ServiceNow’s AI Platform, acting as a control tower for roaming fault resolution.

When a roaming issue occurs, the system:

• Identifies which network domain is affected

• Pinpoints where the issue originated

• Surfaces relevant performance and fault data

• Automatically routes and tracks resolution tasks across carriers

Instead of multiple human teams exchanging emails and ticket IDs, AI-driven workflows coordinate the process. Fault tickets flow automatically, and recovery actions can begin in near real time.

The approach also promises proactive detection. By analyzing cross-network data, carriers may be able to spot systemic roaming issues before customers begin flooding support lines.

That shift—from reactive to predictive operations—is central to telecom’s broader automation push.

Built on Industry Standards

The collaboration also leans on standards-based interoperability.

The operational model incorporates MEF 113 Trouble Ticketing Business Requirements and Use Cases from Mplify. By grounding the solution in open specifications, the companies aim to reduce fragmentation and make the framework scalable across additional operators globally.

Standards matter in roaming. Without common definitions and processes, automation can’t extend beyond bilateral integrations. If the model proves portable, it could serve as a template for broader industry adoption.

Why This Matters Now

International travel has rebounded sharply, and seamless connectivity is increasingly expected—not appreciated as a bonus.

At the same time, telecom operators face margin pressure and rising operational complexity. 5G cores, virtualized infrastructure, and cross-border traffic flows make troubleshooting more intricate than ever.

Autonomous roaming resolution offers a dual benefit:

• Improved customer experience and trust

• Reduced operational overhead and faster mean time to repair (MTTR)

For operators competing in mature markets, experience differentiation can be as important as pricing or coverage maps.

Technical Validation Underway

The companies confirmed that technical validation is currently in progress, with a commercial launch targeted for the second half of the year.

If deployed successfully, the initiative could signal a broader shift toward cross-carrier automation frameworks—particularly in areas where customer experience depends on coordination beyond a single operator’s domain.

In practical terms, the goal is simple: fewer stranded travelers, more reliable roaming, and standardized inter-carrier processes that scale globally.

In strategic terms, it represents something bigger: a move toward treating telecom operations not as isolated silos, but as interconnected ecosystems managed by AI.

For an industry that has long struggled with fragmentation, that may be the real breakthrough.

Get in touch with our MarTech Experts.

At this year’s Mobile World Congress Barcelona, the telecom AI narrative shifted from copilots to collaboration—between machines.

Assurance specialist Mycom announced a strategic partnership with US-based cloud-native networking provider Mavenir to jointly develop Agentic AI use cases for 4G and 5G networks. The goal: move communications service providers (CSPs) beyond dashboard-driven monitoring toward semi- and fully autonomous network operations.

If the industry’s automation rhetoric is to be believed, that’s the promised land.

From Monitoring to Multi-Agent Autonomy

Traditional OSS (Operations Support Systems) platforms have long provided visibility—alarms, performance metrics, fault tickets. What they haven’t reliably delivered is autonomy.

The Mycom–Mavenir partnership is built around Agent-to-Agent (A2A) integration, connecting Mycom’s GenAie NOC Copilot with Mavenir’s Core Domain Intent Agent and domain-specific network function (NF) AI agents inside the mobile core.

Rather than a single AI assistant advising human operators, this model enables multiple specialized agents to collaborate directly—sharing context, diagnosing issues, and triggering remediation workflows across the network stack.

In practice, that could mean:

• Automated detection of network degradation

• Cross-domain root cause analysis

• Closed-loop remediation without manual ticket escalation

The architecture leverages Mycom’s PrOptima (performance management), NetExpert (fault management), and ProAssure (service quality management) platforms alongside Mavenir’s cloud-native mobile core intelligence.

The implication is clear: instead of humans stitching together insights across tools, AI agents do the stitching—and increasingly, the fixing.

Why Agent-to-Agent Matters for 5G

5G networks are inherently more complex than their predecessors. Virtualized cores, distributed architectures, and dynamic slicing introduce operational variables that strain legacy assurance models.

Manual workflows simply don’t scale.

By enabling secure agent-to-agent communication between OSS-level intelligence and domain-native core agents, Mycom and Mavenir aim to create a blueprint for structured, multi-agent collaboration inside live production networks.

According to Mycom Co-founder and CTO Mounir Ladki, the partnership is focused on operationalizing agentic AI “at scale,” not just experimenting with proofs of concept.

The distinction is critical. Many CSPs have tested AI pilots in isolated domains, only to struggle with integration across broader operational ecosystems.

A structured multi-agent framework, if executed cleanly, could reduce the friction between performance management, fault detection, and service quality enforcement.

Accelerating Toward TM Forum Levels 4 and 5

The collaboration also ties directly into the industry’s push toward higher levels of autonomy as defined by the TM Forum Autonomous Networks framework.

Level 4 and Level 5 autonomy envision networks that self-diagnose and self-optimize in real time, with minimal human intervention. Few operators have reached those stages at scale.

Mavenir’s EVP and CTO Bejoy Pankajakshan described the joint initiative as evolving OSS from a monitoring layer into a true autonomy platform—where assurance and optimization occur automatically and continuously.

That framing reflects a broader shift in telecom strategy. As 5G monetization pressures mount and operating margins tighten, CSPs are looking to automation not just for performance gains, but for cost containment.

Autonomous remediation reduces mean time to repair (MTTR), limits service-impacting incidents, and can lower operational expenditure. The business case is as much financial as technical.

Complementary Strengths

The partnership appears strategically aligned.

Mavenir brings deep domain knowledge in the mobile core, along with AI-driven networking solutions embedded directly into cloud-native architectures. Its agents operate close to the network functions themselves, enabling granular insight and closed-loop control.

Mycom, by contrast, sits higher in the OSS stack, offering end-to-end visibility across performance, faults, and service quality.

The agent-to-agent integration effectively connects domain-level intelligence with cross-network orchestration. That layered approach could help CSPs extract more value from existing OSS investments rather than replacing them outright.

In a market where rip-and-replace transformations are both risky and expensive, augmentation through AI may be more palatable.

Industry Context: From Assisted to Autonomous

The telecom sector has spent years pursuing automation through scripts, RPA, and rule-based systems. But rule engines break under the variability of modern, software-defined networks.

Agentic AI introduces a more adaptive model—systems capable of reasoning across context, collaborating with other agents, and taking action based on evolving conditions.

Still, real-world deployment raises questions:

• How are agent decisions audited for compliance and reliability?

• What guardrails prevent cascading automated errors?

• How seamlessly do agents integrate with legacy OSS environments?

These are non-trivial challenges, particularly in live 4G/5G networks supporting millions of subscribers.

If Mycom and Mavenir can demonstrate stable, secure multi-agent operations in production environments, they may provide a credible roadmap for CSPs aiming to reach higher autonomy levels without destabilizing operations.

The Bigger Picture

Telecom operators are under pressure to do more with flat or declining revenue growth. Network complexity is increasing, while tolerance for outages is decreasing.

Agentic AI offers a compelling narrative: networks that detect, diagnose, and fix themselves in near real time.

But autonomy in telecom isn’t a single leap—it’s a series of coordinated integrations across domains.

By formalizing agent-to-agent collaboration between assurance platforms and core network intelligence, Mycom and Mavenir are betting that the path to autonomous operations lies not in one super-agent, but in structured cooperation between many.

If successful, the partnership could mark a meaningful step toward Level 4 and 5 networks—where assurance isn’t just monitored, but executed automatically.

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